Identification of a diagnostic marker to detect freshwater cyanophages of filamentous cyanobacteria

Cyanophages are viruses that infect the cyanobacteria, globally important photosynthetic microorganisms. Cyanophages are considered significant components of microbial communities, playing major roles in influencing host community diversity and primary productivity, terminating cyanobacterial water blooms, and influencing biogeochemical cycles. Cyanophages are ubiquitous in both marine and freshwater systems; however, the majority of molecular research has been biased toward the study of marine cyanophages. In this study, a diagnostic probe was developed to detect freshwater cyanophages in natural waters. Oligonucleotide PCR-based primers were designed to specifically amplify the major capsid protein gene from previously characterized freshwater cyanomyoviruses that are infectious to the filamentous, nitrogen-fixing cyanobacterial genera Anabaena and Nostoc. The primers were also successful in yielding PCR products from mixed virus communities concentrated from water samples collected from freshwater lakes in the United Kingdom. The probes are thought to provide a useful tool for the investigation of cyanophage diversity in freshwater environments.

Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

The detection of dense harmful algal blooms (HABs) by satellite remote sensing is usually based on analysis of chlorophyll-a as a proxy. However, this approach does not provide information about the potential harm of bloom, nor can it identify the dominant species. The developed HAB risk classification method employs a fully automatic data-driven approach to identify key characteristics of water leaving radiances and derived quantities, and to classify pixels into “harmful”, “non-harmful” and “no bloom” categories using Linear Discriminant Analysis (LDA). Discrimination accuracy is increased through the use of spectral ratios of water leaving radiances, absorption and backscattering. To reduce the false alarm rate the data that cannot be reliably classified are automatically labelled as “unknown”. This method can be trained on different HAB species or extended to new sensors and then applied to generate independent HAB risk maps; these can be fused with other sensors to fill gaps or improve spatial or temporal resolution. The HAB discrimination technique has obtained accurate results on MODIS and MERIS data, correctly identifying 89% of Phaeocystis globosa HABs in the southern North Sea and 88% of Karenia mikimotoi blooms in the Western English Channel. A linear transformation of the ocean colour discriminants is used to estimate harmful cell counts, demonstrating greater accuracy than if based on chlorophyll-a; this will facilitate its integration into a HAB early warning system operating in the southern North Sea.

Drivers and effects of Karenia mikimotoi blooms in the western English Channel

Naturally occurring red tides and harmful algal blooms (HABs) are of increasing importance in the coastal environment and can have dramatic effects on coastal benthic and epipelagic communities worldwide. Such blooms are often unpredictable, irregular or of short duration, and thus determining the underlying driving factors is problematic. The dinoflagellate Karenia mikimotoi is an HAB, commonly found in the western English Channel and thought to be responsible for occasional mass finfish and benthic mortalities. We analysed a 19-year coastal time series of phytoplankton biomass to examine the seasonality and interannual variability of K. mikimotoi in the western English Channel and determine both the primary environmental drivers of these blooms as well as the effects on phytoplankton productivity and oxygen conditions. We observed high variability in timing and magnitude of K. mikimotoi blooms, with abundances reaching >1000 cells mL�1 at 10 m depth, inducing up to a 12-fold increase in the phytoplankton carbon content of the water column. No long-term trends in the timing or magnitude of K. mikimotoi abundance were evident from the data. Key driving factors were identified as persistent summertime rainfall and the resultant input of low-salinity high-nutrient river water. The largest bloom in 2009 was associated with highest annual primary production and led to considerable oxygen depletion at depth, most likely as a result of enhanced biological breakdown of bloom material; however, this oxygen depletion may not affect zooplankton. Our data suggests that K. mikimotoi blooms are not only a key and consistent feature of western English Channel productivity, but importantly can potentially be predicted from knowledge of rainfall or river discharge.